Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes

Building on the systematic methodology described earlier, this section presents the experimental outcomes and interprets their significance for maternal mental health prediction. The results are categorised into classification and regression tasks, with a focus on the effectiveness of ensemble methods versus individual classifiers. To begin, we explore the performance of classification models before delving into regression analysis.

Table 3 includes a thorough assessment of six machine learning techniques dedicated to pregnant women’s mental health classification. Results showed that Random Forest yielded the most effective performance by a combination of 97.82% accuracy and 96.81% F1-score during mental health tests. The model produces exceptional precision-recall metrics so it tracks complex psychological health data across various situations. Support Vector Machine (SVM) and Decision Tree achieved 100.00% recall success when used for identifying mental health conditions in pregnant patients during operational deployments. The precision rate computation of Random Forest models is responsible for additional false positives when correct positive detections are included outside of the original results. The accuracy of standard forecasting models which merged Logistic Regression with Gaussian Naive Bayes was equivalent to ensemble forecasting results, yet they did not implement adaptive capabilities. The Multilayer Perceptron (MLP) network demonstrated average performance by properly interpreting processed nonlinear mental health patterns. Random Forest ensemble methods prove their merit in mental health prediction through their role as a suitable fit solution because of their strong capabilities in service delivery forecasting.

Table 3 Performance comparison of machine learning models for mental health classification.

Table 3 displays the results from machine learning model testing through accuracy evaluation, as well as precision and recall measurements, and F1-score assessment. Random Forest, together with Support Vector Machine (SVM), leads the performance rankings. At the same time, Random Forest demonstrates 97.82% accuracy and SVM 93.79% accuracy, along with 100% recall accuracy, which ensures proper detection of all positive cases. The combination of precise and recall performance scores (F1-score) at 96.81% for Random Forest and 96.79% for SVM confirms their capability for making accurate predictions. The performance metrics of Decision Tree and Logistic Regression demonstrate 91% accuracy but maintain almost perfect recall capacity at 100%, and their F1-scores fall to 91.81% and 91.80% due to higher false positive occurrences. The F1-score of Gaussian Naive Bayes reaches 93.79% accuracy, but its 93% recall rate implies the algorithm fails to identify positive cases sometimes, thus yielding a minimal decrease in F1-score. Multilayer Perceptron (MLP) generates the least effective results, along with 92.79% accuracy, yet a 92% recall rate, during which it fails to identify some positive cases, leading to its inferior F1-score outcome. The most reliable models are Random Forest and SVM because they achieve both high accuracy and recall scores. Still, Decision Tree and Logistic Regression provide good results at the cost of additional trade-offs, and Gaussian Naive Bayes and MLP demonstrate subpar performance because of their subpar recall and F1-scores.

A joint presentation of ROC curves and their AUC values demonstrates the Random Forest and Support Vector Machine (SVM) model capability in Fig. 2 for binary classification purposes. A Random Forest model reached an AUC of 0.92, thus proving its exceptional capability to separate different classes. Its ability as a classification tool makes Random Forest a dependable methodology when resolving classification problems. The SVM model achieves a score of 0.91 in AUC, indicating robust classification capabilities, although it falls just behind Random Forest. Nevertheless, SVM remains a powerful model tool, especially when dealing with complex or non-linear datasets. The Decision Tree and Logistic Regression models generate their ROC curves within Fig. 3. A Decision Tree model produced an AUC of 0.86, indicating satisfactory results, but no match was found against the superior models in part because of overfitting effects in complex datasets. Logistic Regression demonstrated an AUC of 0.91, which matched that of SVM and proved particularly suitable for linearly separable problems, thus making it an excellent alternative for datasets with basic structures. Figure 4 shows that Multilayer Perceptron (MLP), together with Gaussian Naïve Bayes, demonstrated strong results, both achieving an AUC of 0.89, but these scores failed to surpass the other tested models. The sophisticated MLP model performed at the same level as SVM and Logistic Regression, reaching an AUC of 0.91, to demonstrate its ability to model intricate non-linear associations. Random Forest and SVM stood out for their high performance, yet different features were beneficial based on the level of dataset complexity and model specifications.

The different machine learning models’ classification performances appear in Figs. 5 and 6, and 7 through their confusion matrix displays. The evaluation results in Fig. 5 demonstrate that Random Forest and Support Vector Machine (SVM) produce outstanding outcomes through perfect recall and minimal or no incorrect negative detections, indicating their capability to identify all positive cases precisely. Random Forest evaluates predictions more effectively than other models because it detects fewer incorrect outcomes yet maintains accurate results. The confusion matrices for Decision Tree and Logistic Regression in Fig. 6 show perfect recall but higher false positive rates that affect their precision, along with their F1-scores. Gaussian Naïve Bayes, together with Multilayer Perceptron (MLP), demonstrate decreased recall levels of 93% and 92%, respectively, in Fig. 7 because these models fail to detect some positive instances. The improved number of incorrect predictions indicates these algorithms should be avoided in scenarios requiring perfect detection of positive instances.

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Performance of random forest

Random Forest delivers exceptional predictive results because it merges its resistance to error and capability to detect complex data patterns, and its ability to analyse unequal datasets. The predictive model uses multiple decision trees to control overfitting, thus achieving reliable prediction results. Random Forest provides decisive advantages in health datasets through its non-linear pattern modelling functionality since mental health relationships do not show linear characteristics. Random Forest achieves success in mental health classifications thanks to its functionality with imbalanced datasets which enables it to handle distribution inequalities between classes.

The analysis combines Random Forest with Logistic Regression and Gaussian Naive Bayes to achieve excellent performance measures and clear interpretation of results.

Random Forest achieves 97.82% accuracy through its ability to resist data variations so it works optimally with multi-dimensional datasets that require complex analysis.
Although achieving 91.79% accuracy the model remains practical for clinical practice because it maintains simple interpretation.
Due to its 93.79% precision rate Gaussian Naive Bayes is suitable as a quick prediction tool despite its precise dataset performance.

The combined methods provide an extensive framework to tackle mental health prediction by maintaining accuracy standards in addition to user-friendly operation.

As illustrated in Fig. 8, the visual representation of model performance clearly emphasizes the dominance of ensemble methods like Random Forest. The figure captures the balance across accuracy, precision, recall, and F1-score, underscoring the model’s ability to manage class imbalance and optimize predictions effectively. Random Forest’s consistent performance sets a benchmark for mental health classification tasks. Building on these classification insights, we next evaluate regression models to predict continuous outcomes related to maternal health. Regression Model Performance: Table 4 demonstrates the examination of varied regression techniques used for continuous maternal health prediction. Both Decision Tree Regressor and Random Forest Regressor proved to be perfect prediction tools because they achieved an R² score of 1.000, which demonstrated no errors within the data. The verification of both precision and reliability depends on the minimal Mean Squared Error values. Linear Regression attained a perfect model evaluation through its R² score of 1.000 with small MSE while assessing linear relationships among dataset variables. The neural network in MLP achieved results equal to Linear Regression through R² score 0.9999 and small MSE values that demonstrate accurate modelling of complicated relationships. Support Vector Regressor achieved an R² score of 0.956, but it struggled to monitor intricate maternal welfare information because its error assessment proved unfavourable compared to linear and tree-based methods. The evaluated outcomes from tree-based methods and linear regression demonstrate suitability in developing automated maternal care systems for mental health diagnosis.

Table 4 Performance metrics of regression models for mental health prediction.

Results highlight the exceptional predictive power of tree-based models and traditional linear methods for regression tasks, reinforcing their reliability for maternal health monitoring.

Figure 9 provides a clear visual comparison of the Mean Squared Error (MSE) and R² scores across different regression models used for mental health prediction. The exceptionally low MSE and perfect R² scores of the Random Forest Regressor (4.5767 × 10⁻⁸, R² = 1.000) and Decision Tree Regressor (6.3581 × 10⁻⁸, R² = 1.000) demonstrate their superior predictive capabilities and perfect fit to the training data. Linear Regression also achieves a perfect R² score, but with a nearly negligible MSE, suggesting its effectiveness for linear relationships. The Support Vector Regressor shows the worst MSE (0.0099) so far, indicating a certain complexity in learning a more complicated pattern of data, but otherwise, it is also doing quite well with the R2 value of 0.956. The Multilayer Perceptron, finally, works nearly perfect (MSE = 1.6351 × 10⁻⁵, R2 = 0.9999), which proves that neural networks can be successfully applied to regression tasks as well. The experimental analysis shows that Random Forest model performed better than other models in both classification and regression tests. Due to its resistance, high precision, and low error rates, it can be considered a feasible instrument in developing AI-based solutions in the psychological support of mothers. These findings outline the importance of applying state-of-the-art machine learning algorithms to detect and address psychological health problems at the early stages of pregnancy. Having decided on the performance of the model, we offer a special loss function that allows focusing on the problem of imbalance between classes and achieving even better results in terms of predicted accuracy. The study applied a certain loss functional that considered the requirements of decreasing the imbalance of classes and the quality of performance. There are two important operational features of the function maximize model performance:

1.

Cross-entropy loss (CE loss): During classification operations, Cross-Entropy Loss (CE Loss) serves as the primary criterion for assessing forecasting accuracy between expected outcomes and actual class targets. The penalty system’s operational mechanism encourages the model to generate fewer errors and increase classification accuracy rates.
2.

F1 score penalty: A special part of the model design, the F1 Score Penalty prevents dominant class bias and shields underrepresented infrequent categories. Complete loss magnification occurs for the model when the F1 score stays low, making the model search for optimal precision-recall balance.

The union of these individual components forms a practical, balanced loss function. The model maintains consistent performance by preventing errors while upholding the fair treatment capabilities of all classes due to this method. The proposed loss function leverages the synergy between accurate results and a balanced distribution of classes, making it a highly effective tool for generating fair and dependable predictions. The Execution of the custom loss function leads to an explanation of training procedures, together with described asset reduction patterns.

Table 5 details the model training process throughout 10 epochs, where it displays the training loss pattern together with related observations. The model indicated a progressive decline in loss values, starting from epoch 1 (2.5880) and reaching epoch 8 (2.4382), which signifies that the model learned effectively while improving its parameter optimisation. The model reached its learning peak during epoch 8 when loss was at its minimum point, indicating it achieved balanced class results and minimum misclassification rates. At epoch 9 (2.4564), loss starts to climb, which means the beginning of overfitting behaviour because the model trains specifically for training data points without adequate generalisation. The loss value from epoch 10 (2.4724) demonstrates possible stabilization that creates a balanced performance by preventing excessive overfitting of the model. A correctly managed training approach becomes visible through this pattern, which proves the model’s capability to handle class imbalance while providing strong performance results across diverse mental health indicators. Verification of these training results proves the model’s universal application competence needed for clinical maternal mental health evaluation.

Table 5 Training loss and observations across epochs.

Figure 10 provides a clear visual comparison of the Mean Squared Error (MSE) and R² scores across different regression models used for mental health prediction. The exceptionally low MSE and perfect R² scores of the Random Forest Regressor (4.5767 × 10⁻⁵, R² = 1.000) and Decision Tree Regressor (6.3581 × 10⁻⁸, R² = 1.000) demonstrate their superior predictive capabilities and perfect fit to the training data. Linear Regression also achieves an ideal R² score, but with a nearly negligible MSE, suggesting its effectiveness for linear relationships. The Support Vector Regressor, while still performing well with an R² of 0.956, shows a higher MSE (0.0099), indicating some difficulty in capturing complex data patterns. The Multilayer Perceptron also achieves near-perfect performance (MSE = 1.6351 × 10⁵, R² = 0.9999), highlighting the strength of neural networks for regression tasks. These results collectively underscore the efficiency and reliability of tree-based and linear models in maternal health monitoring, as shown by the loss values mapped against epoch numbers on their two axes. The time-based loss pattern analysis indicates successful model parameter learning, as the loss decreases over time. The custom loss function successfully handles class imbalance and implements both cross-entropy loss and an F1 score penalty function system. The model training process optimises performance for minority class examples based on loss measurements between epochs, according to research data. Further regularization methods and extended training duration may be necessary because the loss curve exhibits minimal variations throughout the later epochs. The implementation of cross-entropy loss together with an F1 score penalty function makes it possible to enhance imbalanced data management and creates an advanced evaluation framework. Upcoming research will study better methods to adjust loss function parameters as well as advanced approaches to maximize performance potential²³.

Figure 11 displays loss value distribution against epoch counts across its two axes after the analysis. The time-based analysis of the chart indicates that the learned model parameters follow a downward trend. The implemented custom loss function addresses class imbalance issues by combining cross-entropy loss with an F1 score penalty function. The cross-entropy loss reaches a stable point at 0.3724 during epoch 21,000, which indicates successful optimisation. An analysis of loss data across training epochs demonstrates how the model optimisation method achieves its best performance for processing minority class examples. Later epoch tallies in loss measurement indicate potential overfitting needs, which demand more regularisation methods or longer training duration. The model builds on the evaluation methods and is more effective in dealing with imbalanced data using its F1 score penalty and cross-entropy loss. To achieve improved execution results, the further investigation will consider more effective approaches to enhance the elements of the loss function when developing new tactics. according to the conclusions made during the training sessions we are now going to discuss all the outcomes. The loss was rapidly decreased during the initial steps of the model, which suggests the maximum performance and strong abilities to adjust parameters. The model had been doing its best between the majority and minority classes until the end of the training process in the mid-training epochs. On the final training steps, the model experienced a small rise in loss, probably due to local minimum stabilization or overfitting. Even though the issues related to the class imbalance are minimized, and the model performance is enhanced with the help of a custom loss function, further convergence optimization could be achieved with the help of learning rate adjustments and regularization strategies. Following technical analysis, we do offer our extensive tool that would facilitate maternal mental health. Mental health status is the foundation of wellbeing during pregnancy. The mental stress experienced by expectant mothers leads to anxiety, emotional tension and depression, which adversely affect the developing baby and the expectant mother. Such symptoms can be aggravated by circumstances caused by medical circumstances, physical discomfort and emotional strain. Chronic conditions occur when psychological challenges throughout pregnancy are unchecked and unattended to. Mental health monitoring technologies particularly in pregnancy are desired and useful.

Non-invasive and highly practical mental health tracking technologies concentrate on pregnancy and are needed. Because yoga and natural therapies have proved to be effective in the management of stress and anxiety, they can be part of comprehensive mental health screening systems. Our mental health tracker supports the psychological well-being of pregnant women with directions on yoga training, natural medications, and symptom recording. The latter aims to identify symptoms and offer preventive care, which are the core objectives of this tool. Mood tracking and psychological tests of the mental health tracker, as well as tailored support interventions, allow keeping mental balance at a stable level throughout the pregnancy. The many attributes the tool monitors include detecting depressive thoughts and sadness (Feeling Sad), mood swings and emotional turbulence (Irritability), assessing the quality of sleep and insomnia (Trouble Sleeping), focusing problems (Problems Concentrating), stress-related eating behavior (Overeating), anxiety levels and situations that cause them (Feeling Anxious), feelings of guilt (Feelings of Guilt), maternal bonding problems (Problems Bonding with Baby), and automated alerts in case of high-risk behavior such as suicide attempts. Each characteristic causes customized suggestions, including yoga asanas and herbal remedies. Specific recommendations are drinking lavender or chamomile tea to calm anxiety and breathing exercises along with meditation. Warm milk and aromatherapy with essential oils are also recommended to people who cannot sleep, as well as yoga in Shavasana and Childs Pose. Dynamic mood tracking, Dark Mode, and an intuitive design of the tracker also lead to greater user satisfaction. The program simplifies the data by bar-graph representation of mental health statistics. The 31-day mood visualization allows individuals to be more self-aware and pin down things that may disorder their emotional balance. The application may be developed in future to perform machine learning on previous mental health trends, provide real-time consultation with a medical professional, and offer AI-based mental health assistance capabilities. The proposed changes will provide specialized and narrowed solutions. The prenatal mental health tracker is an innovative test to comprehensive mental health assessment and enhancement of pregnant women in an inviting, scientific manner. This program will offer the user the power to be in control of their mental wellbeing, with the capacity to keep track of crucial metrics and provide preventative treatment alternatives such as yoga and natural remedies.

The Mental Health Tracker System, designed for pregnant women to track and improve their mental health, is shown in Fig. 12. Weekly mental health evaluations, bar graph visualizations, and daily mood tracking are all included in the system’s user-friendly interface. By skilfully illustrating mood swings over a 31-day span, the bar graphs help users spot trends and pinpoint possible emotional causes. Through daily check-ins, users can record symptoms like worry, impatience, and difficulty sleeping. The system also provides individualized wellness recommendations based on the recorded symptoms, such as yoga poses and natural cures. Overall, by offering concise, evidence-based insights regarding changes in mental health throughout pregnancy, this tool promotes self-awareness and preventive care (Figs. 13, 14).

The images can be used together to provide a comprehensive view of how the system operates, with options of personalized precaution (Fig. 15), mental health assessments (Fig. 14), and symptom selection (Fig. 13). Such visual elements on the system include toggle dark mode (Fig. 16), bar graphs (Fig. 17), and mental health overviews (Figs. 18 and 19), which enable users to monitor the changes in their emotional conditions. The visualizations provided by this system provide an easy-to-use way of doing data-driven mental health promotion. By providing the solid framework of combining the state-of-the-art machine learning processes with the helpful tool of enhancing maternal mental health, the study is paving the way to the future important breakthroughs in the related area. The project progressed, with an initial design, through the development of an operational system prototype, written in Python, and running on a secure internet platform. The Random Forest classifier was trained on annotated data, and after passing systemic testing with synthetic user data, without compromising privacy of the data, pre-clinical technical feasibility was attained.

To work efficiently, the system requires a digital basis that implies a flexible frontend and backend infrastructure, secure cloud storage services provided by AWS and Azure, and the possibility of instant information management with secure authentication mechanisms. In order to ensure the sustainability of operations in three critical areas, including software development, management of data privacy, and research on artificial intelligence, different professions ought to come up with multidisciplinary teams. The deployment meets the local data protection regulations including the ethical institutional regulations, the HIPAA standards, and the GDPR principles. The encryption of secure health information is facilitated by built-in consent procedure tools that provide users with encryption as well as auditing privileges. The explainable AI modules and protection features of this platform allow end users and medical staff to comprehend AI outputs. The platform will automatically create a model retraining procedure when it receives user and clinical feedback on the platform when the feedback process is activated. The system establishes immediate safety alert messages to medical staff regarding patients with suicidal behaviours and severe psychological issues, thus facilitating fast appropriate clinical attention.

Core machine learning principles receive in-text definitions within the text through precision and recall concepts connected with F1-score and ensemble learning definitions while providing source citations for foundational AI content. The document uses diagnostic criteria from both the World Health Organization (WHO) and the American Psychological Association (APA) together with clinical guidelines to show mental health problems in pregnant women. The expert-reviewed studies confirm that yoga and naturopathy approaches successfully reduce both pre-and-postnatal anxiety and stress and depression symptoms The researchers apply transfer of disciplinary knowledge by using statements such as “From an AI standpoint…” and “Clinically, this implies…” throughout the text The updates seek to establish a better comprehension connection between research scientists and clinical staff and policymakers who work together with integrated healthcare specialists.

Experimental setup: integration of naturopathic and yoga interventions

Pregnant women face rising mental health challenges because stress, anxiety, depressive symptoms and sleep problems and emotional dysregulation affect various groups of expectant mothers. Medical professionals favor complementary non-invasive treatments as alternatives to conventional drugs because these options pose reduced risks to fetal development. The strategy incorporates naturopathic and yoga-based practices, supported by scientific evidence from peer-reviewed research publications.

Medical therapies from Noruo therapy can work together with yoga methods to give patients full biopsychosocial healthcare, which addresses physical needs along with neural requirements and mental wellness. Pregnant individuals who practice prenatal yoga, incorporating pranayama techniques and mindfulness meditation, can achieve control over their HPA axis and decrease cortisol production, while increasing their parasympathetic response. Maternal emotional strength benefits from combined physiological effects through which fetal development remains protected, along with maternal health improvement and decreased risks of fetal structural abnormalities and early pregnancy complications.

The combination of natural Ashwagandha supplements, aromatherapy, and dietary advice helps patients achieve the most effective results in terms of hormone stability, enhanced sleep quality, and balanced neurochemical levels. The interventions create agreements for personalized pre-birth care objectives dedicated to pregnant women. The following table presents an extensive research summary of high-impact studies which validate the effectiveness of yoga and naturopathic techniques for maternal populations.

Table 6 Evidence-based naturopathic and yoga interventions for maternal mental health.

Table 6 introduces an evidence-based model to support the implementation of yoga and naturopathic interventions within maternal mental health care. Various interventions listed in the table have received high-quality peer-reviewed approval which demonstrates their physiological and psychological and emotional advantages for pregnant women. The research used experimental tests that united machine learning algorithms with yoga methods and naturopathic treatment frameworks, which have scientific validation. The systematic combination leads to an expanded system which enables individuals to create drug-free and satisfactory remedies for pregnancy mental health concerns. Healthcare systems must develop standardised guidelines through randomised controlled trials and meta-analyses, and long-term follow-up evaluations for these practices to become operational. Impact of Natural Treatments on Maternal Mental Health: The health condition of pregnant mothers significantly affects their emotional state as well as the well-being of their newborns. Natural therapies provide the consumer with comprehensive methods, which are not surgical in nature and which help in dealing with stress and anxiety as also emotional balance. The following are evidence-based natural therapy methods, which enhance maternal mental health:

1.

The quality of sleep and reduction of anxiety are the twofold benefits of this relaxation technique of putting feet in lukewarm water that works by enhancing blood circulation.
2.

The massage therapy on pregnant women instills less stress and a calm mind on the expectant women due to the physical alleviation of pain and lowering cortisol level.
3.

Walking barefoot enables the body to ground on the surface of nature either by touching grass or sand thus reducing the level of stress and as a result enabling one to connect with nature better.
4.

When listened to, therapeutic music re-enforces moods and reduces anxiety and fear and also establishes an emotional contact with the developing baby.
5.

Breathing exercises and pranayama have a calming effect on the mindROM because they regulate the autonomic nervous system and enable emotional balance and mental focus. The research studies have shown that the combined health benefits that come along with the use of several natural therapies are more superior than the use of each separate one.

Studies have shown that natural therapies are most effective when used to treat patients together as the efficiency of a certain form of treatment becomes lowered when applied in the isolation of other forms of treatment. Among the most intriguing ones, the following have been found: Prenatal massage, music therapy, and dietary changes create a significant effect, stressed-management and reduction of anxiety. This is the period that the benefits of probiotics in mental health are manifested hence intake of the good bacteria in the diet leads to postnatal emotional stability. The Ghost Studying therapeutic techniques, which are applied to eliminate the tension and the anxiety, which pregnant women experience, requires the invention of breathing techniques. Over the decades, pranayama has proved itself as a yoga technique of stress reduction by virtue of imparting therapeutic effects that are not limited to oxygenation transparency and mental relaxation. The old breathing techniques and the energy management techniques stimulate the neurological system to provide temporary psychological results.

1.

Hand Stretch Breathing enables one to achieve physical and mental relaxation as the hands are moved according to the breathing patterns. Exercise which comprises of deep breathing exercises results in a complete release of imminent stress and tension. The inner calmness is the result of mental clarity and attention to the breath which can be felt already during the practice of movements with the attention to the breath.
2.

Nadi Shodhana stabilizes the body through Alternate Nostril Breathing, which operates by controlling respiration to manage heartbeat functions. The practice of education generates mental serenity and body relaxation, making it an excellent therapy for emotional control and anxiety management.
3.

Brahmari (also known as Humming Bee Breath) requires you to make hum sounds while exhaling, which leads to physical body vibration, which brings peace to both your mind and nervous system. The vagus nerve activation from hearing humming sounds reduces stress while providing deep relaxation to the audience. The practice of building effectiveness needs to be performed 5 to 7 times.

Figures illustrate the stages and techniques of these breathing exercises, providing a visual guide for effective practice:

Figures 20, 21, 22, 23 and 24 depict several types of yoga breathing exercises, including Hand Stretch Breathing, Nadi Shodhana, and Brahmari Pranayama. Each technique delivers stress reduction, mental serenity, and relaxation while presenting an overall method to boost well-being.

Mudras for Stress Relief: Hand positions known as mudras draw from yoga philosophy to restore five body elements (water, fire, earth, air, and space) thus creating stress relief for pregnant women. The recovery of both physical and mental harmony leads to optimised energy circulation while reducing stress. The following five hand gestures serve pregnant women well:

1.

Through Prana Mudra practice, women can gain vitality and enhance their immune system function.
2.

The Varuna Mudra works on body water levels to establish emotional stability, together with physical health benefits.
3.

Gyan Mudra: Enhances concentration, wisdom, and mental clarity.

The application of Vayu Mudra controls air element balance to minimize anxiety along with restlessness in pregnant women.

Yoga breathing methods are shown in Fig. 25 through for Hand Stretch Breathing together with Nadi Shodhana in Fig. 26 and Brahmari Pranayama shown in Fig. 27. These breathing techniques lead users to relaxation while reducing stress together with promoting tranquillity in the mind so users attain well-being in a complete way. Mudras as hand gestures originating from yoga teachings facilitate the balance of body elements (water, fire, earth, air and space) which enables stress relief. The balance established through these techniques stimulates energy circulation for better stress reduction. The following collection of mudras serves pregnant women the most benefit:

1.

The Prana Mudra strengthens both immune health and vitality in a person’s body.
2.

The Varuna Mudra regulates water elements in your body to establish emotional stability and physical balance.
3.

Gyan Mudra: Enhances concentration, wisdom, and mental clarity.

The air element balancing mechanism in Vayu Mudra helps expectant women reduce their nervousness and calm their restlessness.

The benefits of instruction of Prana Mudra and Varuna Mudra appear in Figs. 28 and 29, followed by advantages and usage information of Gyan Mudra and Vayu Mudra presented in Figs. 30 and 31. Mudra practices, along with prenatal massage, yoga breathing, and therapeutic music, can help preserve maternal mental health through modified diets and barefoot walking activities. Combining all wellness system elements delivers quick relaxation and sustains positive emotional outcomes, which benefit both mothers and their developing babies. The conclusion of our analysis consolidates all observed effects resulting from the adoption of natural healing techniques. A comprehensive analysis of practical healthcare obstacles in treating psychological conditions in pregnant women would strengthen the research. Additional expansion of the three main topics is necessary. Practical Challenges: Providing psychological aid to expecting mothers presents multiple execution difficulties. Pregnant individuals encounter three significant challenges to mental health care access: pregnancy stigma, low availability of trained mental health experts, together with financial barriers, and cultural pressures. The manuscript will gain both practice-relevant content and enhanced practical value through open discussion of the mentioned issues. Potential Solutions: The manuscript lacks an assessment of possible solutions which address the encountered obstacles. The digital landscape contains mobile health applications along with telemedical services that enable pregnant women from underserved locations to obtain mental health care more easily. Interdisciplinary relationships between mental health specialists and obstetricians, and technologists should be actively developed to deliver a comprehensive therapeutic framework for patients. Pregnant women would benefit from community-integrated support systems, which include peer support groups as well as online forums to create a stigma-free environment.

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Performance of random forest

Experimental setup: integration of naturopathic and yoga interventions

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